Hide

[Mahitraja]

You[]()

[Mahitraja]

ghost ecSys

Datasets

We add all of the datasets into the datasets folder with the user and item groups. Each folder in datasets folder is relevant to one dataset and each of which contains a folder called groups. The groups folder includes to subfolders, items and users. The items folder contains short-head and long-tail item groups while the users folder includes user group files based on two user grouping methods (i.e., interaction and consumption).

How to Run?

We already provided the preprocessing datasets, user and item groups file. To run the model you can jump to 2. Model: UFR.ipynb section.

1. Datasets: grouping.ipnb

To generate user and item grouping one can use the grouping notebook. The outputs of this notebook are added into the dataset folder.

2. Model: UFR.ipynb

In order to make easy to use the reproducibility of URF mode, we create a notebook which can be run on Google Colab easily. Thus, you only need to config the dataset and then run the cells and get the final reuslts in CSV files. To do this, the follwing steps need to be taken:

• First, you can open the notebook of URF model using Run model or copy and pate the follwing link in your browser:

  https://colab.research.google.com/github/rahmanidashti/FairRecSys/blob/main/UFR.ipynb

• Next, you can config the dataset using the follwoing cell in the notebook:

  # dataset congfig
  ds_names = ["AmazonOffice"]
  is_implicit = False
  ds_users = ['2']
  ds_items = ['020']

  where ds_names is the name of the dataset, is_implicit indicate the feedback type, ds_users refer to the user grouing methods (005 -> interactions and 2 -> popular consumption), and ds_items indicate the item grouping method which is 020, indicating short-head and long-tail items grpouing according to top 20% popular items. You can check all the datasets, their attributes, users and items grouping in dataset folder.

• Finally, when you run the model the reuslts for each recommendation algorithms will be stored in a CSV file called results_DATASET_MODEL.csv including the follwing columns:

  Dataset: Name of the dataset
  Model: Name of base recommendation algorithm
  GUser: User group
  GItem: Item group
  Type: Fairness type which is either N (i.e, fairness-unaware) or C (i.e., user fairness-aware)
  User_EPS: User epsilon
  Item_EPS: -
  ndcg_ALL: NDCG for all users
  ndcg_ACT: NDCG for advaneteged/active users
  ndcg_INACT: NDCG for disadvaneteged/inactive users
  Pre_ALL: Precision for all all users
  Pre_ACT: Precision for advaneteged/active users
  Pre_INACT: Precision for disadvaneteged/inactive users
  Rec_ALL: Recall for all users
  Rec_ACT: Recall for advaneteged/active users
  Rec_INACT: Recall for disadvaneteged/inactive users
  Nov_ALL: Novelty for all users
  Nov_ACT: Novelty for advaneteged/active users
  Nov_INACT: Novelty for disadvaneteged/inactive users
  Cov_ALL: Coverage for all users
  Cov_ACT: Coverage for advaneteged/active users
  Cov_INACT: Coverage for disadvaneteged/inactive users
  Active_GAP: GAP for all advaneteged/active users
  Inactive_Gap: GAP for all disadvaneteged/inactive users
  Short_Items: The numebr of short-head recommended items
  Long_Items: The numebr of long-tail recommended items
  All_Items: The numebr of all recommended items

  Note we included the all raw result files in the results folder. We use these files to generate the plots, figures, and tables in the paper. We also can consider them for further analysis.

3. Analysis and Plots: analysis.ipynb

You can run the analysis to generate the plots and the analysis.

The current version of grouping.ipynb and analysis.ipynb are the local version that means you can run them on your local machine. You need to download the datasets. We plan to provide a server like Google Colab plus a feature to write and store the generated user and item group files as well as plots into FairRecSys repository.

Tables (Results on Paper)

Due to space limitations we add the results on the Epinions and Last.fm datasets into the paper. However, we add the final results of all datasets into the tables folder accroding to each user grouping method (i.e., interactions and popular consumption). There, we have two folders, 005 and 2, for user grouping based on interactions (005) and popular consumption (2).

Plots

We added all the reported plots as well as the other plots that we did not add into the paper due to space consideration in the plots folder. These plots are generated by analysis.ipynb notebook.

Team

Hossein A. Rahmani, Web Intelligence Group, UCL

Mohammadmehdi Naghiaei, University of Southern California

Mahdi Dehghan, SBU, Shahid Beheshti University

Mohammad Aliannejadi, IRLab, University of Amsterdam

Citation

If you use our source code, dataset, and experiments for your research or development, please cite the following paper:

@inproceedings{rahmani2022fairrecsys,
  title={Experiments on Generalizability of User-Oriented Fairness in Recommender Systems},
  author={Hossein A. Rahmani, Mohammadmehdi Naghiaei, Mahdi Dehghan, Mohammad Aliannejadi},
  booktitle={SIGIR},
  year={2022}
}

Acknowledgements

TBA

Contact

If you have any questions, do not hesitate to contact us by h.rahmani@ucl.ac.uk or rahmanidashti@gmail.com, we will be happy to assist

[Mahitraja]

You[]()

I love you